cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Saluky
Contact Email
luke4line@yahoo.com
Phone
-
Journal Mail Official
jurnalsistemcerdas@gmail.com
Editorial Address
-
Location
Kota bekasi,
Jawa barat
INDONESIA
Jurnal Sistem Cerdas
Published by Asosiasi Prakarsa Indonesia Cerdas
ISSN : -     EISSN : 26228254     DOI : -
Core Subject : Science, Education, Engineering,
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering
Jurnal Sistem Cerdas dengan eISSN : 2622-8254 adalah media publikasi hasil penelitian yang mendukung penelitian dan pengembangan kota, desa, sektor dan kesistemam lainnya. Jurnal ini diterbitkan oleh Asosiasi Prakarsa Indonesia Cerdas (APIC) dan terbit setiap empat bulan sekali.
Arjuna Subject : Umum - Umum
Articles 195 Documents
 16   17   18   19   20 
A Multivariate LSTM Approach for Monthly Rice Production Forecasting in East Java Firdausi, Hasanur Mohammad; Utomo, Satryo Budi; Rahardi, Gamma Aditya; Prasetiyo, Dani Hari Tunggal
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.595

Abstract

Accurate forecasting of rice output is essential for improving regional food security planning, particularly in East Java Province, which serves as a major national rice granary. This study develops a Long Short-Term Memory (LSTM) model to predict rice production using monthly data on production and harvested area from 2018 to 2024. The methodology includes data preprocessing, normalization, sequence construction with a sliding window, training of a multivariate LSTM model, and performance evaluation using mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Results show that the LSTM model achieves superior predictive accuracy, with an MAE of 95,030.16, RMSE of 120,229.01, and MAPE of 16.64%, significantly outperforming baseline Moving Average and Linear Regression models. While the model effectively captures seasonal production trends, some inaccuracies remain during periods of anomalous production values. These findings suggest that the LSTM model is effective for projecting rice production and may provide a foundation for early warning systems and regional food distribution strategies. Further improvements could be realized by integrating climate variables or adopting a hybrid model architecture to enhance predictive precision.
WebSocket-Based Smart Surveillance Camera for Real-Time Detection of Occupational Health and Safety PPE Non-Compliance in Industrial Areas Sabarto, Rivaldi Azis; Sulistiyowati, Indah; Syahrorini, Syamsudduha; Wisaksono, Arief
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.597

Abstract

In industrial settings, ensuring adherence to Occupational Health and Safety (OHS) Personal Protective Equipment (PPE) regulations continues to be a crucial challenge. The creation of a WebSocket-based smart surveillance camera system for the real-time identification and reduction of PPE infractions is discussed in the paper. The proposed system includes an ESP32-S3 microcontroller accompanied by an OV5640 camera module, acting as an edge-processing embedded platform. The Edge Impulse machine learning framework was used to train image classification and detection models, enabling efficient low-latency inference directly on the device. A websocket enabled web server streams video frames in real time for constant monitoring, with instant display using regular browsers without wasting bandwidth. Experimental results demonstrate that even with limited computational resources, the system is able to perform on-device inference with very high responsiveness and good detection accuracy. This technology provides a scalable and affordable way to enhance OHS compliance monitoring in industry, reduce reliance on manual supervision, and encourage proactive risk mitigation methodologies.
IoT-Based Package Drop Box System Using Arduino Uno and ESP32-CAM Simatupang, Frengki; Sigiro, Marojahan M.T; Sinambela, Eka Stephani; Manalu, Istas Pratomo
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.581

Abstract

The rapid growth of e-commerce activity in Indonesia has led to a surge in package delivery volumes, resulting in increased workloads for couriers and a higher risk of delivery failures when recipients are not present at the delivery location. This issue demands an innovative solution that can address logistical challenges in an automated, secure, and efficient manner. This study aims to design and implement an IoT-Based Package Drop Box system that integrates Arduino Uno, Wemos D1 Mini, ESP32-CAM, and the GM66 barcode scanner, along with a web interface and WhatsApp notification service. The methodology follows a prototype engineering approach consisting of need analysis, system design, implementation, and hardware-software testing phases. The test results demonstrate that the system can successfully verify package tracking numbers via barcode scanning at an optimal distance of 10–19 cm, automatically unlock the box, capture images inside the drop box using ESP32-CAM, and send real-time delivery notifications to users via WhatsApp. The system is also capable of storing data locally when the internet connection is lost and synchronizing it once the connection is restored. The findings conclude that the integrated system provides a practical and reliable solution to common delivery issues and has the potential to be further developed for smart home environments or broader Internet of Things-based logistics systems.
Use of Cosine Similarity, Manhattan Distance, and Jaccard Similarity Methods to Improve the Accuracy of Manual Payment Evidence Validation in ERP Applications Muslim, Muslim; Amira, Sheilla; Usino, Wendi
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.594

Abstract

Manual validation of payment receipts in Enterprise Resource Planning (ERP) applications often faces challenges in terms of Accuracy, especially when payment data must be matched with existing transactions. Data mismatches can lead to recording errors and increase the burden of manual verification. This study aims to improve the Accuracy of payment receipt validation by comparing three Similarity methods: Cosine Similarity, Jaccard Similarity, and Manhattan Distance. In this research, Optical Character Recognition (OCR) is utilized to validate scanned images of payment receipts. By using OCR, data from receipt images can be automatically extracted into text format for further processing. The experimental results show that Cosine Similarity delivers the best performance, with a Precision of 100%, Recall of 90%, and Accuracy of 90%. On the other hand, Jaccard Similarity failed to identify any valid data, resulting in 0% across all evaluation metrics. Meanwhile, Manhattan Distance achieved high Precision (100%) but performed poorly in Recall and Accuracy, both at 10%. Based on these findings, Cosine Similarity is recommended as the most effective method for enhancing OCR-based payment validation in ERP systems. This study also opens the opportunity to develop hybrid approaches, combining Cosine Similarity and Manhattan Distance methods to further improve overall system performance.
Predicting Student Final Grades Using Random Forest Algorithms and Linear Regression Mahyudi, Mahyudi; Endaryono; Ristiawan, Rifki
Jurnal Sistem Cerdas Vol. 8 No. 3 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i3.618

Abstract

The increasing adoption of intelligent systems in higher education has encouraged the use of data-driven approaches to predict students’ academic performance. Accurate prediction models are essential to support early intervention and informed academic decision-making. This study aims to conduct a comparative analysis between Random Forest and Linear Regression algorithms in predicting students’ final academic scores. The dataset consists of assessment components, including quiz scores, assignment scores, and midterm examination (UTS) scores, which are used as predictor variables. The data were divided into training and testing sets with a ratio of 80:20. Model performance was evaluated using accuracy, error metrics, and feature importance analysis. The experimental results indicate that Random Forest outperforms Linear Regression in terms of predictive accuracy and robustness. Furthermore, both models consistently identify midterm examination scores as the most influential factor affecting students’ final performance. These findings demonstrate that ensemble-based learning methods are more suitable for academic performance prediction and can serve as a reliable foundation for intelligent academic support systems in higher education.

Page 20 of 20 | Total Record : 195

 16   17   18   19   20 

Filter by Year

2018 2025


Reset
Filter By Issues
All Issue Vol. 8 No. 3 (2025) Vol. 8 No. 2 (2025) Vol. 8 No. 1 (2025) Vol. 7 No. 3 (2024) Vol. 7 No. 2 (2024) Vol. 7 No. 1 (2024) Vol. 6 No. 3 (2023) Vol. 6 No. 2 (2023) Vol. 6 No. 1 (2023) Vol. 5 No. 3 (2022) Vol. 5 No. 2 (2022) Vol. 5 No. 1 (2022) Vol. 4 No. 3 (2021) Vol. 4 No. 2 (2021) Vol. 4 No. 1 (2021) Vol. 3 No. 2 (2020): Riset dan Inovasi Sistem Cerdas pada Penanggulangan Wabah Covid-19 Vol. 3 No. 3 (2020): Kecerdasan Artifisial pada Rekayasa Biomedis Vol. 3 No. 1 (2020): Artificial Intelligence untuk Indonesia Vol. 2 No. 3 (2019): Transformasi dan Inovasi Kota Cerdas Vol. 2 No. 2 (2019): Smart Transportation Vol. 2 No. 1 (2019): Artificial Intelligence for Smart Society Vol. 1 No. 2 (2018): Internet of Things for Smart Society Vol. 1 No. 1 (2018): Smart System More Issue